Successful patient-oriented research requires that careful thought be given to trial design and conduct in order to provide the most accurate and efficient results. Heterogeneity of risk characteristics in acute ischemic stroke patients increases the likelihood of unmatched clinically relevant patient characteristics in comparative research and can result in underestimates of treatment effect. The use of accurate prediction models in the design of trials and analysis of data can address these potentially confounding issues and improve the accuracy and efficiency of stroke studies. This proposal aims to use logistic regression techniques to: 1: Assess the relationships between each of 11 routinely available acute physiology measures (blood pressure, heart rate, temperature, glucose, blood urea nitrogen, creatinine, sodium, potassium, billirubin, hematocrit and white blood count) and death in 2,250 critically ill acute ischemic stroke patients to create the Acute Physiology of Stroke Score (APSS). 2: Combine the APSS with a previously validated acute ischemic stroke model that predicts devastating outcome (nursing home level disability/death) and validate it in an independent data set. We will use the stroke subgroup of the Cerner critical illness data set as well as the RANTTAS and NINDS tPA clinical trial data sets to develop the score, validate it and improve our existing prediction model. This research will result in an improved method of predicting individual probability of devastating outcome in ischemic stroke patients and allow improved clinical stroke trial design and analysis. It will also provide a patient-oriented research basis for individual mentoring of trainees as well as contribute to the development of a novel 1 month clinical research elective to be offered to house staff and neuroscience students at the University of Virginia with the goal of increasing the proportion of trainees with clinical research training.